Human Object Classification Using Dual Tree Complex Wavelet Transform and Zernike Moment - Transactions on Large-Scale Data-and Knowledge-Centered Systems - page 100

data) into one of the two categories viz. human objet and non-human object. The
proposed method is compared with other state-of-the-art methods proposed by Khare
et al. [2], Dalal and Triggs [7], Lu and Zheng [9], Renno et al. [14], and Chen et al.
[15] as well as classification results obtained by using Zernike moment as a feature
set, classification results obtained by using discrete wavelet transform as a feature set.
Comparison has been done in terms of average classification accuracy, TPR (Recall),
and PPR (Precision). Quantitative experimental analysis shows that the proposed
method gives better classification results than other discussed methods. Main
advantage of the proposed method is that: the proposed method can detect human
objects in complex background, as well as the proposed method can detect human
objects of different size.
Acknowledgement. This work supported in part by Council of Scientific and Industrial
Research (CSIR), Human Resource Development Group, India, Under Grant No. 09/001/
(0377)/2013/EMR-I.
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